Personalized QoS-Aware Web Service Recommendation and Visualization

Abstract With the proliferation of web services, effective QoS-based approach to service recommendation is becoming more and more important. Although service recommendation has been studied in the recent literature, the performance of existing ones is not satisfactory, since 1) previous approaches fail to consider the QoS variance according to users’ locations; and 2) previous recommender systems are all black boxes providing limited information on the performance of the service candidates. In this paper, we propose a novel collaborative filtering algorithm designed for large-scale web service recommendation.

Abstract con… Different from previous work, our approach employs the characteristic of QoS and achieves considerable improvement on the recommendation accuracy. To help service users better understand the rationale of the recommendation and remove some of the mystery, we use a recommendation visualization technique to show how a recommendation is grouped with other choices. Comprehensive experiments are conducted using more than 1.5 million QoS records of real-world web service invocations. The experimental results show the efficiency and effectiveness of our approach.

Existing System WEB services are software components designed to support interoperable machine-to-machine interac¬tion over a network. The adoption of web services as a delivery mode in business has fostered a new paradigm shift from the development of monolithic applications to the dynamic setup of business process. In recent years, web services have attracted wide attentions from both industry and academia, and the number of public web services is steadily increasing.

Architecture Diagram

System specification HARDWARE REQUIREMENTS Processor : intel Pentium IV Ram : 512 MB Hard Disk : 80 GB HDD SOFTWARE REQUIREMENTS Operating System : windows XP / Windows 7 FrontEnd : Java BackEnd : MySQL 5

CONCLUSION In this paper, we have presented an innovative approach to web service recommendation and visualization. Different from previous work, our algorithm employs the character¬istic of QoS by clustering users into different regions. Based on the region feature, a refined nearest-neighbor algorithm is proposed to generate QoS prediction. The final service recommendations are put on a map to reveal the under¬lying structure of QoS space and help users accept the recommendations. Experimental results show that our approach significantly improves the prediction accuracy than the existing methods regardless of the sparseness of the training matrix. We also demonstrate that the online time complexity of our approach is better than the traditional CF algorithms.

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